In this section, we analyze the quality of visual representations learned by model on a broad range of computer vision tasks as there's no general agreement on what qualifies for universal or “ideal” visual representation [1]}. To this end, we benchmark the robustness of models to distribution shift in Sec. REF , fine-grained recognition performance on challenging datasets such as iNaturalist18  [2]} in Sec. REF  including the application of model in wildlife conservation efforts, image retrieval (copy detection) in Sec. REF , and representation learning via linear-probe to test image classification performance on more than 25 standard object and scene datasets including ImageNet-1K  [3]} (object centric), Places205  [4]} (scene centric) and PASCAL VOC07  [5]} (multi-label) in Sec. REF . We also compare our model performance with state-of-the-art supervised and self-supervised learning on ImageNet-1K on computer vision datasets capturing variety of applications such as OCR, activity recognition in videos, scene recognition, medical and satellite images, structured datasets (to test localization) in and show full results in Appendix .
